Resultado de búsqueda
Chroma is an open-source vector database. Pick up an issue, create a PR, or participate in our Discord and let the community know what features you would like.
- Documentation
Chroma is the AI-native open-source vector database. Chroma...
- Blog
Chroma raises $18M seed round. building the AI-native...
- 🔑 Getting Started
Chroma is an AI-native open-source vector database. It comes...
- 🔌 Integrations
Chroma maintains integrations with many popular tools. These...
- About
Our investors#. Chroma raised an $18M seed round led by...
- Terms of Use
Chroma may collect, generate, and derive Usage Data (as...
- Privacy
For example, when you contact us to learn more about Chroma,...
- 📏 Telemetry
In Chroma's Backend Using Environment Variables#. Set...
- Documentation
- Overview
- Features
- Use case: ChatGPT for ______
- Embeddings?
- Get involved
Chroma - the open-source embedding database.
The fastest way to build Python or JavaScript LLM apps with memory!
| | Docs | Homepage
|
•Simple: Fully-typed, fully-tested, fully-documented == happiness
•Integrations: 🦜️🔗 LangChain (python and js), 🦙 LlamaIndex and more soon
•Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster
•Feature-rich: Queries, filtering, density estimation and more
For example, the "Chat your data" use case:
1.Add documents to your database. You can pass in your own embeddings, embedding function, or let Chroma embed them for you.
2.Query relevant documents with natural language.
3.Compose documents into the context window of an LLM like GPT3 for additional summarization or analysis.
What are embeddings?
•Read the guide from OpenAI
•Literal: Embedding something turns it from image/text/audio into a list of numbers. 🖼️ or 📄 => [1.2, 2.1, ....]. This process makes documents "understandable" to a machine learning model.
•By analogy: An embedding represents the essence of a document. This enables documents and queries with the same essence to be "near" each other and therefore easy to find.
•Technical: An embedding is the latent-space position of a document at a layer of a deep neural network. For models trained specifically to embed data, this is the last layer.
•A small example: If you search your photos for "famous bridge in San Francisco". By embedding this query and comparing it to the embeddings of your photos and their metadata - it should return photos of the Golden Gate Bridge.
Chroma is a rapidly developing project. We welcome PR contributors and ideas for how to improve the project.
•Join the conversation on Discord - #contributing channel
•Review the 🛣️ Roadmap and contribute your ideas
•Grab an issue and open a PR - Good first issue tag
•Read our contributing guide
Release Cadence We currently release new tagged versions of the pypi and npm packages on Mondays. Hotfixes go out at any time during the week.
Chroma DB is an open-source vector store used for storing and retrieving vector embeddings. Its main use is to save embeddings along with metadata to be used later by large language models. Additionally, it can also be used for semantic search engines over text data.
16 de mar. de 2024 · What is Chroma DB? Chroma DB is a vector database system that allows you to store, retrieve, and manage embeddings. It can be used in Python or JavaScript with the chromadb library for local...
Chroma is an open-source embedding database designed to store and query vector embeddings efficiently, enhancing Large Language Models (LLMs) by providing relevant context to user inquiries. In this tutorial, I will explain how to use Chroma in persistent server mode using a custom embedding model within an example Python project.
